arXiv subjects

M. M. Rizvi

Publications and source records attributed to M. M. Rizvi.

4 recordsLinked to original sources

arXiv2026

Green Inventory Management: Leveraging Multiobjective Reverse Logistics

The paper proposes a novel Economic Production Quantity (EPQ) inventory model within a reverse logistics framework, addressing new and repaired products with varying quality and demand patterns. The model integrates production and remanufacturing rates as functions of lot sizes and cycle numbers to develop a feasible inventory cost function. A key contribution of the study is formulating a Mult objective optimization framework that simultaneously minimizes inventory costs and accounts for environmental sustainability by considering greenhouse gas (GHG) emissions and energy consumption during production processes. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model, with integer constraints on lot sizes and cycle counts and a continuous return rate. Numerical case studies taking test problems from existing literature are used to validate the model through extensive sensitivity analyses. Both mathematical optimization and heuristic optimization methods are applied to solve Mult objective optimization problems, and Pareto fronts are illustrated along with the interpretation of the results.

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arXiv2026

A Multiobjective Optimization Framework for Irrigation Water Allocation

Sustainable irrigation planning requires balancing economic benefits with environmental flow requirements under increasing climatic and resource constraints. Building on the irrigation optimization framework developed by Ullah and Nehring, this study improves the analytical depth of existing approaches by expanding the feasible decision space and systematically characterizing the full economic--environmental trade-off spectrum. Two single-objective formulations, maximizing net agricultural benefit and minimizing environmental flow deficiency (EFD), are solved to identify boundary solutions that define the limits of the feasible space. These are subsequently integrated into a multiobjective optimization framework using scalarization and evolutionary search techniques to generate a high-resolution Pareto frontier. Numerical experiments on the Muhuri Irrigation Project reveal three key outcomes: (i) a complete scenario view with profits ranging from $0.2 \times 10^{9}$ to $1.497 \times 10^{9}$ and EFD values between 0 and 1200~GL, where 1200~GL represents the theoretical annual maximum under a uniform monthly environmental flow target of 100~GL; (ii) explicit trade-offs demonstrating that higher economic returns are consistently associated with greater ecological shortfalls; and (iii) a computationally efficient approach capable of generating nearly 1000 Pareto-optimal solutions within a short runtime ($\sim$10 seconds), substantially improving solution resolution compared to earlier studies. By transforming point-based optimization into comprehensive trade-off mapping, the proposed framework provides a more informative basis for scenario analysis and decision support in irrigation water allocation, offering a practical extension to existing optimization approaches.

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arXiv2025

A new mathematical model and algorithm for the optimal path to intercept a moving target

This paper is concerned with determining the shortest path for a pursuer aiming to intercept a moving target travelling at a constant speed. To address this challenge, we introduce an efficient mathematical model outlined as an optimal control problem. The proposed model is based on Dubin's path, where we concatenate two possible paths: a left-circular curve or a right-circular curve followed by a straight line. We develop and explore this model, providing a comprehensive geometric interpretation, and design an algorithm tailored to implement the proposed mathematical approach efficiently. Extensive numerical experiments involving diverse target positions highlight the strength of the model. The method exhibits a remarkably high convergence rate in finding solutions. We compare the proposed model and demonstrate its advantages through examples. For experiment purposes, we utilized the modelling software AMPL, employing a range of solvers to solve the problem. Subsequently, we simulated the obtained solutions using MATLAB, demonstrating the efficiency of the model in intercepting a moving target. The proposed model distinguishes itself by employing fewer parameters and making fewer assumptions, setting the model simplifies the complexities, and thus, makes it easier for experts to design optimal path plans.

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arXiv2024

Optimizing Inventory Management through Multiobjective Reverse Logistics with Environmental Impact

We present novel mathematical models for inventory management within a reverse logistics system. Technological advancements, sustainability initiatives, and evolving customer behaviours have significantly increased the demand for repaired products. Our models account for varying demand levels for newly produced and repaired items. To optimize overall costs with constrained scenarios, we formulated mixed integer programming problems. Solution procedures for the proposed problems are introduced, and the accuracy of these solutions has been validated through numerical experiments. Additionally, we address the cost of waste disposal as an environmental concern. This paper develops a multiobjective mathematical model and provides an algorithm for the Pareto solution. Various scalarization techniques are utilized to identify the Pareto front, and a comparison of these techniques is presented.

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